Psychometric properties of cognitive screening for patients with cerebrovascular diseases A systematic review
Bibliographic record
Abstract
Screening instruments are ideal for acute clinical settings because they are easy to apply, fast, inexpensive and sensitive for specific samples. However, there is a need to verify the psychometric properties of screening in stroke patients. OBJECTIVE: This study investigated the psychometric properties (methodological procedures) of cognitive screening for patients with cerebrovascular diseases. METHODS: A systematic review of papers published on PsycINFO, Web of Knowledge, PubMed and Science Direct (2005 to 2016) was performed. RESULTS: A total of 55 articles remained after applying exclusion criteria. The samples ranged from 20 to 657 patients. Most articles evaluated elderly individuals with four to 13 years of education who had experienced ischemic or hemorrhagic stroke. There was a tendency to find evidence of validity for criteria and to analyze the sensitivity/specificity of the instruments. Although the studies frequently used the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) to seek evidence of validity and reliability, the use of these instruments among stroke patients has been criticized due to their psychometric properties and the neuropsychological functions evaluated. CONCLUSION: Although there is no gold standard screen for assessing adults post-stroke, instruments devised specifically for this population have shown promise. This review helps both researchers and clinicians to select the most appropriate screen for identifying cognitive impairment in adults post-stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".